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CPU limits

How to Limit CPU Usage on a VPS Running a 24/7 YouTube Stream

Use a hard quota on the systemd service or Docker container running your encoder, and test the real stream so a CPU cap does not cause encoding lag.

By VGSources Team 6 min read
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To cap CPU use without letting a 24/7 YouTube stream silently fall behind, limit the systemd service or Docker container that owns FFmpeg or OBS with a hard CPU quota, then check encoding progress and YouTube stream health. Use systemd CPUQuota= for a systemd service or Docker --cpus for a container. A CPU weight is only a relative scheduling preference, not a ceiling, and no single quota is safe for every VPS and stream.

Choose the control that actually caps CPU

Apply the limit to the service or container containing the encoder process and its child processes. Limiting only a shell or launcher that exits after starting FFmpeg may not constrain the encoder’s process group.

Control What it changes Hard ceiling? Use it when
systemd CPUQuota= Maximum CPU time for a service cgroup Yes FFmpeg or OBS runs as a systemd service
Docker --cpus or Compose cpus CPU capacity available to a container Yes The encoder runs in Docker
systemd CPUWeight= or Docker --cpu-shares Relative CPU access when workloads compete No You want to favor other work during contention, without a fixed cap
systemd AllowedCPUs= or Docker --cpuset-cpus Which CPUs the process or container can run on No; it restricts placement, not CPU time You have a specific pinning or isolation reason

Docker notes that, by default, a container has no resource constraints and may use as much of a resource as the host kernel scheduler allows (Docker resource constraints). A quota can free capacity for other services, but setting it too low can throttle encoding and cause lag. Relative weights do not cap an otherwise idle workload.

Limit a systemd service with CPUQuota

Set the quota in the service unit

systemd expresses CPUQuota= as a percentage of one CPU. For example, 20% permits at most one-fifth of one CPU; values above 100% allow more than one CPU-equivalent. The value below is only an illustration, not a recommended setting:

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Put the setting under the unit’s [Service] section. For a unit you manage directly, edit it with sudo systemctl edit --full your-stream.service, replacing the unit name with your actual service. If you use a drop-in instead, ensure it modifies the intended service and section.

Reload, restart, and verify

  1. Save the unit file or drop-in.
  2. Reload systemd’s unit definitions: sudo systemctl daemon-reload.
  3. Restart the stream service: sudo systemctl restart your-stream.service.
  4. Check that it is running: systemctl status your-stream.service.
  5. Observe CPU use, encoder progress, and YouTube stream health while the representative stream is running. If encoding cannot keep pace, raise the quota or reduce the workload, then test again.

Consult the systemd resource-control manual for options and behavior relevant to your installed systemd version. A parent cgroup can impose additional restrictions, so the effective resources may be lower than the unit setting suggests.

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Limit a Docker container

Use a CPU ceiling

Set --cpus when creating the container. For instance, Docker documents --cpus=".5" as a limit of at most half of one CPU. In a Compose service, cpus specifies the number of CPUs the service may use. Adapt the example to your existing image, volumes, network, command, and restart policy; do not replace those settings with a bare example that would change how your stream runs.

docker run --cpus=".5" ... your-image

Docker implements CPU limits through its CFS quota and period mechanism. Resource-limit support depends on the VPS kernel and Docker configuration. Docker notes that rootless resource flags have requirements involving cgroup v2, systemd, and controller delegation; check its resource-constraints documentation if the limit is rejected or has no apparent effect.

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Do not substitute CPU shares for a cap

--cpu-shares is a relative weight: it influences access when containers compete for CPU, but does not impose a ceiling when spare CPU is available. Use --cpus when you need a hard limit. For live container metrics, run docker stats and inspect the stream container while it is encoding.

Tune the quota against the actual stream

Measure before and after

  1. Identify whether FFmpeg or OBS is running directly under systemd, inside a container, or under another supervisor. Apply the cap at the layer that owns its process tree.
  2. Record the baseline CPU use and whether encoding progress stays in step with the input. FFmpeg’s -stats_enc_pre and -stats_enc_post options can write per-frame information before encoding and as encoded packets are received; see the FFmpeg documentation for exact usage.
  3. Choose a cautious initial quota based on observed load and the capacity you need to reserve for other work. There is no universal safe percentage: CPU generation, resolution, frame rate, codec, filters, and concurrent processes all affect demand.
  4. Run a representative test with the real video, audio, and motion, then monitor both the encoder and the live stream’s health. YouTube says, “Make sure to test before you start your live stream.”
  5. Adjust the cap in small steps. If encoding falls behind or YouTube reports stream problems, restore capacity or reduce the encoding workload and retest.

Use the control that matches your objective: quotas set a CPU-time ceiling; weights share CPU relatively under contention; affinity restricts where work runs. Choosing more CPU capacity helps only if CPU is the bottleneck, and a provider’s advertised estimates cannot establish what your particular stream needs.

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Reduce work only if the changed output is acceptable

Lowering resolution or frame rate can reduce the workload, but it changes what viewers receive. YouTube’s requirements and bitrate guidance depend on codec, resolution, and frame rate; verify the appropriate output for your chosen format and test the result. YouTube’s live encoder guidance lists RTMP/RTMPS, H.264, H.265 (HEVC), and AV1, frame rates up to 60 fps, CBR, and a recommended two-second keyframe interval that should not exceed four seconds. These platform settings may change; check the current YouTube live encoder settings for your stream.

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Check CPU and network as separate bottlenecks

A CPU quota does not fix insufficient upload bandwidth, and lowering bitrate is not the same as lowering CPU usage. YouTube recommends testing upload speed and allowing 20% upload headroom; see its streaming tips. Monitor stream health alongside CPU and encoder progress so you can distinguish a constrained encoder from a network issue.

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SpaceNode’s 2026 provider guide gives indicative estimates—not an independent benchmark or universal VPS specification—of 1 core, 1 GB RAM, and 2 Mbps upload for a 480p FFmpeg loop; 2 cores, 2 GB RAM, and 5 Mbps for a 720p FFmpeg loop; and 2 cores, 4 GB RAM, and 8 Mbps for a 1080p FFmpeg loop. Its 1080p OBS estimates are 4 cores, 4 GB RAM, and 10 Mbps for software encoding, or 2 cores plus a GPU, 4 GB RAM, and 10 Mbps for GPU encoding. Actual needs vary by workload and host; a GPU is not guaranteed on a generic VPS, nor is GPU encoding necessarily supported by its FFmpeg build. See the provider’s 24/7 streaming guide as an attributed sizing reference, not a rule.

Common CPU-limit problems and fixes

  • The stream still uses all available CPU: Check that you set a quota, not only CPUWeight= or --cpu-shares, and that the limit applies to the service or container running the encoder.
  • The encoder falls behind after the change: The quota may be too restrictive for this workload. Increase it or reduce resolution, frame rate, or other encoding work; verify quality and stream health after the adjustment.
  • A container limit is rejected or appears ineffective: Check kernel, Docker, and cgroup support, including rootless delegation requirements, against Docker’s resource documentation.
  • CPU looks acceptable but YouTube reports instability: Check encoder progress and outbound bandwidth separately. Test the upload path and retain the recommended upload headroom; a CPU graph alone cannot diagnose a network bottleneck.
  • You are considering a GPU setting: Confirm the VPS actually provides a GPU and that the encoder build supports the relevant acceleration path. Do not assume a generic VPS has usable GPU encoding.
  • You pinned the service to CPUs but CPU time is still high: Affinity controls placement, not total CPU time. Use a quota if you need a ceiling.

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